Reserving Study Notes
Chapter 3: Reserving
IFOA SP7 Syllabi Covered
Understanding reserving methods, bases, and issues: This involves grasping various actuarial techniques used to estimate future claim liabilities, such as Chain Ladder, Bornhuetter-Ferguson, and stochastic models. It also covers the underlying assumptions of these methods (e.g., stability of development patterns, independence of claims), the regulatory and accounting bases for setting reserves (e.g., IFRS 17, US GAAP, Solvency II in Europe), and common practical challenges or complexities encountered in the reserving process, such as data limitations (scarce or unreliable data), impacts of economic changes (inflation, interest rates), changes in legal environments, and the inherent uncertainty in long-tail claims like general liability or workers' compensation.
Evaluation of reserving results and communication of uncertainty in reserving: This entails the critical assessment of reserve estimates using various diagnostics and validation techniques (e.g., actual vs. expected analysis, sensitivity testing, back-testing of prior estimates), comparing results from different methods to triangulate a robust estimate, and the effective articulation of the inherent variability and potential range of future outcomes to stakeholders such as management, regulators, and shareholders. This includes quantifying prediction error, distinguishing between possible and reasonable outcomes, and presenting reserves with appropriate confidence levels (e.g., for solvency capital requirements).
3.1 The Reasons for Calculating General Insurance Reserves
A general insurer is liable to pay claims covered by a relevant policy upon receiving the premium. This liability arises upon the occurrence of an insured event, which crystallizes the insurer's obligation to compensate the policyholder even if the actual payment is deferred or the full extent of the loss is not immediately known. This is a fundamental principle of insurance where risk is transferred from the policyholder to the insurer, creating a future obligation for the insurer.
Claims may not be settled during the policy period and could take a significant amount of time to finalize, often extending many years beyond the policy's expiry. This necessitates the creation of reserves to financially account for these future obligations, ensuring that the insurer has sufficient funds set aside for claims that have already occurred but have not yet been paid. This is particularly true for 'long-tail' classes of business, such as casualty or liability insurance.
Main reasons for delays:
Time between an incident and policyholder awareness: For example, latent diseases (e.g., asbestos-related claims, mesothelioma) where symptoms may not manifest for decades, or property damage that is not immediately apparent following an event like a slow leak or structural damage after an earthquake that is only discovered much later. Environmental pollution claims are another example, where the full extent of damage may only become clear years after the polluting event.
Delay in reporting the loss by the insured: Policyholders might delay reporting minor incidents until they accumulate or await full assessment before notification. In some cases, third parties injured by an insured may take time to bring a claim, or the policyholder might be unaware of their coverage for a specific event.
Delay in passing claims from intermediaries to insurers: Brokers or agents (e.g., managing general agents) may take time to process and forward claims notifications and documentation to the insurer, especially in complex cases, international exposures, or where multiple parties are involved.
Time to gather details to assess the claim's value: This includes obtaining police reports for motor accidents, medical records for bodily injuries, legal opinions, engineering reports and repair quotes for property damage, and expert opinions that are essential for accurate valuation and for determining liability and quantum. This can be a lengthy process, particularly when liability is contested.
Time until the injured party's condition stabilizes: Particularly for bodily injury claims, such as workers' compensation or general liability, the full extent of injury and subsequent costs (e.g., long-term care, rehabilitation, loss of future earnings, future medical expenses) may not be known for several years, requiring interim payments or a delayed final settlement until a maximum medical improvement (MMI) is reached.
Delay in agreeing on settlement value and issuing payment: This can be due to protracted negotiations between parties (insurer, insured, third party), legal disputes that go to arbitration or court, or complex quantification of damages, especially in large liability cases or class-action lawsuits.
Time factors involved: These temporal distinctions help actuaries track and analyze claims development and payment patterns, providing critical insights for reserving.
Policy inception time: The date the insurance contract begins, typically marking the beginning of the risk period for a specific policy. This is used for 'underwriting year' analysis.
Event occurrence time: When the insured incident actually takes place, which is crucial for assigning claims to the correct underwriting or accident year cohort for reserving purposes. This is used for 'accident year' analysis.
Loss reporting time: When the insurer is notified of the claim, distinguishing between claims incurred but not reported (IBNR) and reported claims. This defines the 'reporting delay'.
Values agreement time: When the settlement amount is finalized between all parties, which is a key milestone for reserve accuracy as it signifies commitment to a specific payment.
Payment times: The actual dates when payments are made by the insurer, which can be a single lump sum or a series of interim payments over time. This illustrates the 'payment pattern'.
Ultimate settlement time: The final date when all aspects of a claim are resolved and all payments are complete, at which point the claim is considered closed and the reserve can be released.
Insurers estimate ultimate amounts for claims, reserving them, and keeping a reserve equal to ultimate claims expected minus known paid amounts. This is a forward-looking process, anticipating total future outgo for claims that have already been incurred but not yet fully settled. The goal is to ensure that sufficient financial provisions are made to cover the final cost of these claims, whether they are reported or not, paid or outstanding.
Reserve necessity varies over time; as a claim matures and more information becomes available (e.g., final medical reports, court judgments, repair invoices), actuaries gain more knowledge and the uncertainty around the ultimate settlement value generally decreases as the settlement time approaches. This means early estimates for very young claims are often subject to wider ranges of uncertainty than later estimates for more mature claims.
3.1.1 Basic Components of Reserves
Claims/loss reserve is due to these inherent delays, equaling the sum of:
Case reserves: These are specific estimates set by claims adjusters or based on formulas for individual claims that have been reported to the insurer but are not yet fully settled. They are often based on the apparent facts, documentation, and severity of each claim as it is initially reported and developed. For example, a claims adjuster might set a {\$}50,000 reserve for a reported motor accident based on initial repair estimates and medical reports, which are then updated as new information becomes available.
IBNR reserves (Incurred But Not Reported): Reserves for claims that have been incurred (i.e., the insured event has happened) but have not yet been reported to the insurer. This component ensures that claims that are in the system but not yet visible as individual reported claims are still accounted for, based on statistical projections of historical reporting patterns. This is a crucial component, especially for long-tail lines of business where reporting delays can be significant.
Further divided into:
Pure IBNR reserves: This specifically refers to claims that have occurred but have not yet been notified to the insurer. These claims are entirely unknown to the insurer at the valuation date. An example would be a slip-and-fall incident that occurred last month but the injured party has not yet formally contacted the insurer.
IBNER reserves (Incurred But Not Enough Reserved/Reported): This component covers the potential for development on existing reported claims where the ultimate cost might exceed the current case reserve. It also captures situations where claims have been reported but inadequately reserved (hence, 'not enough reserved'), or claims that have been reported but their full extent has not yet emerged (e.g., a known injury whose long-term impact and costs are still uncertain). For example, a case reserve of {\$}10,000 might have been set for a minor injury, but subsequent medical complications suggest the ultimate cost will be {\$}30,000, requiring an IBNER component of {\$}20,000.
Claims reserves are calculated on a gross basis (i.e., before considering any recoveries from reinsurance or other sources like salvage and subrogation). This represents the total liability the insurer faces. These gross reserves can then be presented net of future premiums or outwards reinsurance share (the portion the reinsurer is liable for). 'Net reserves' thus reflect the insurer's actual financial exposure after accounting for expected recoveries from third parties or reinsurers.
Insurers must hold reserves to cover full expected claim costs; the adequacy of the unearned premium reserve (UPR), which covers future claims on the unexpired portion of current policies, must also be checked to ensure it is sufficient to cover related future claims and expenses. The UPR typically represents premiums received for coverage that extends beyond the valuation date. An adequacy check ensures that this premium, after fulfilling its coverage obligations, is sufficient to cover related future claims, acquisition expenses, and other costs, and may require a Premium Deficiency Reserve (PDR) if found inadequate.
3.1.2 Best Estimates and Uncertainty
Actuarial Best Estimate (ABE): This is the probability-weighted mean of potential future claims, considering all foreseeable future cash flows and events. It represents the central expectation of the ultimate cost, without any explicit margins for prudence. For example, if there's a 40% chance of {\$}100 claim, a 30% chance of {\$}200, and a 30% chance of {\$}300, the ABE would be (0.40 \times 100) + (0.30 \times 200) + (0.30 \times 300) = 40 + 60 + 90 = {\$}190. This includes 'Events Not In Data' (ENIDs), which are low-frequency, high-severity events (e.g., a major natural catastrophe not present in historical data or a novel pandemic) that must be accounted for separately through scenario analysis, expert judgment, or specific modeling techniques, as they are part of quantifiable future cash flows even if not historically observed.
Managerial Best Estimate (MBE): The reserve booked in financial accounts which may include a management margin for prudence. This margin is typically added by management or required by regulators to provide an additional buffer against adverse development, unforeseen events, or to reflect a greater level of security desired for financial reporting or solvency purposes. For example, an ABE of {\$}100 might be increased to an MBE of {\$}110 by adding a 10% prudential margin.
Uncertainty in reserves must be communicated effectively because reserving estimates are unlikely to match actual future outgo exactly due to the inherent randomness of future events and limitations of current data and models. This communication is crucial for risk management, capital allocation, and ensuring transparency to regulators and investors. Actuaries must convey not just a single point estimate, but also the range of potential outcomes and the confidence level associated with the booked reserve, which is often more informative than a single point estimate for decision-making.
3.2 Data
3.2.1 Claims Development and Data Triangle
Claims experience over time is represented by evaluating cumulative claims amounts, typically showing how claims values (paid or incurred) accumulate from the point of occurrence or reporting. This allows actuaries to observe the 'run-off' patterns of claims for different periods.
Data Triangle: This is a common actuarial tool, providing a historical illustration of claims development. It organizes claims data by origin period (e.g., accident year, policy year, or underwriting year) and development period (or age of the claims cohort). This structure reflects the pattern of how claims emerge and settle over time, allowing actuaries to observe trends and project future development using methods like Chain-Ladder. The data in the triangle is typically cumulative, showing the total claims (paid or incurred) at successive points in time since the origin period.
Example: A typical cumulative paid claims triangle showing how claims mature over time:
Accident Year | Development Year 1 | Development Year 2 | Development Year 3 | Development Year 4 | ...
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2010 | 396 | 1,333 | 2,050 | 2,300 | ...
2011 | 441 | 1,288 | 1,980 | | ...
2012 | 480 | 1,400 | | | ...
The triangle has implicit dimensions, each representing individual cohort experiences and overall development across years. These implicit dimensions can include calendar year effects (e.g., changes in claims handling processes, economic inflation affecting settlement values, changes in legal precedents), shifts in business mix (e.g., writing more long-tail business), or changes in policy terms (e.g., increased deductibles, new coverage exclusions). Identifying these implicit effects is crucial for adjusting historical development patterns to project future claims accurately.
3.2.2 Data Grouping
Claim Cohorts: Claims are typically grouped by an origin event for analysis. Common groupings include:
Event occurrence (accident year): Groups all claims arising from events that occurred in a specific calendar year, regardless of when the policy was written. This is often preferred because it directly links claims to the actual incidents.
Underwriting year (or policy inception) dates: Groups all claims from policies written (or incepted) in a specific year, regardless of when the incident occurred. This aligns claims with the premium revenue for that underwriting year.
Report year: Groups claims based on when they were reported to the insurer. This is less common for ultimate reserving but useful for analyzing reporting delays.
Cohort and frequency definitions impact development patterns and reserve calculations significantly. Inconsistent grouping or definitions can obscure true trends and lead to biased estimates. For example, if an insurer changes its definition of a 'reported' claim mid-year, historical reporting patterns might no longer be reliable.
3.2.3 Data Types
Different key data types for reserving include:
Claims amounts: Such as paid claims (actual cash outlays made by the insurer to date on a claim), outstanding claims/case reserves (estimates for reported but unsettled claims), and incurred claims (the sum of paid and outstanding claims, representing the current best estimate of total cost for reported claims so far).
Claim numbers: This includes reported claims (notifications received by the insurer), settled claims (claims that have been fully resolved with all payments made), and open claims (reported but not yet settled). Understanding claim counts provides insights into frequency, separate from severity, and can be used in methods like 'Bornhuetter-Ferguson claim count' or average severity analysis.
Loss ratio: The ratio of claims to premiums (\frac{Claims}{Premiums}), which is a key indicator of underwriting profitability and used prominently in the Initial Expected Loss Ratio method. This can be calculated on an ultimate basis (ultimate claims divided by earned premium) or an incurred basis (incurred claims divided by earned premium).
Exposure information: Relevant metrics for risk such as policy counts, sum insured, number of vehicles, or payroll (for workers' compensation). Exposure data helps normalize claims experience and project future claims based on the volume of business written, rather than just historical dollar amounts.
Commissions (paid to brokers/agents), Large losses (individual claims exceeding a certain threshold), Allocated and unallocated loss adjustment expenses (ALAE/ULAE), among others, are also important. ALAE refers to expenses directly tied to specific claims (e.g., legal fees for a specific lawsuit, expert witness fees, investigations), while ULAE covers general claims department overhead not assigned to individual claims (e.g., salaries of claims managers, rent for claims offices). These expenses are generally part of the claims costs and must be reserved for.
3.2.4 Data Subdivisions
Class of business (e.g., motor, property, general liability), Claim type (e.g., bodily injury, property damage, fire), Claim size (e.g., small claims vs. large claims), Region (e.g., specific states or countries), or distribution channel (e.g., direct vs. broker-originated business) are common subdivisions. The aim is for homogeneous grouping (i.e., grouping similar risks together) to ensure that claims within a subdivision behave similarly and are subject to similar development patterns. This needs to be balanced with statistical credibility (ensuring sufficient data in each group for reliable analysis), as too many subdivisions can lead to sparse data and unstable estimates.
3.2.5 Large/Catastrophe Losses and Latent Claims
Special analysis is required for losses arising from catastrophes (e.g., hurricanes, earthquakes, floods, wildfires) or latent claims (e.g., asbestos, environmental pollution, product liability). These claims often have unique development profiles (e.g., very long tails, sudden emergence of many claims), exhibit high severity, and may require different reserving techniques due to their infrequent or long-tail nature and potential for significant upward development far exceeding initial estimates. Catastrophe claims are often modeled using specialized catastrophe models, and latent claims require specific expert judgment and trend analysis.
3.2.6 Benchmark Data
External benchmarks, such as industry data (from rating agencies, trade associations), data from similar insurers, or actuarial consultants, can guide estimates where internal data is insufficient (e.g., for new lines of business, small books of business) or unreliable (e.g., due to significant operational changes). Benchmark data is also valuable for validating internal assumptions and identifying where an insurer's experience deviates significantly from the market, prompting further investigation.
3.3 Bases of Reserving
3.3.1 Reasons for Estimating Reserves
Understanding the ultimate purpose of the reserve estimate significantly impacts the chosen basis for reserves, as different stakeholders have distinct information requirements and regulatory frameworks. Reserves are required for actuarial reporting (e.g., actuarial opinions on reserve adequacy), financial accounting (e.g., for investors and public disclosures), solvency regulation (e.g., to ensure insurer financial strength), and internal management decision-making (e.g., for business planning and performance monitoring).
3.3.2 Use of Different Reserving Bases
Published accounts (e.g., under IFRS 17 in many international jurisdictions, or US GAAP in the United States) adhere to specific accounting standards that dictate how reserves are measured and presented. For instance, IFRS 17 requires specific measurement models for insurance contracts, including a best estimate of future cash flows and an explicit risk adjustment.
Tax purposes often involve specific rules for deductibility of reserves as expenses, which may differ from accounting or solvency bases, influencing the amount that can be set aside for tax purposes.
Solvency accounts (e.g., Solvency II in Europe, Risk-Based Capital in the US, or other local solvency regimes) require reserves that reflect a high level of security to protect policyholders, often at a high percentile (e.g., 99.5th percentile under Solvency II for technical provisions), which includes a best estimate and a risk margin.
Management accounts are for internal use, often incorporating more granular detail or different margins for strategic decision-making, performance analysis, or setting internal targets. These may reflect different levels of prudence or allow for specific analyses not required by external reporting standards.
3.3.3 Reserving Versus Pricing Bases
Reserving methods estimate ultimate costs for past incurred claims, which are already on the books. This involves analyzing historical development patterns of claims that have already occurred. The goal is to true up the ultimate cost of past events.
Pricing methods, in contrast, focus on the expected future claims costs for new business to be written. This involves forecasting future claims behavior for a population of future policyholders and events, often without the same prudential margins used in reserving for known liabilities. Pricing also considers expenses, profit margins, and competition.
While both aim to estimate future claims, their purpose, scope, perspective (retrospective vs. prospective), and embedded prudence levels differ significantly. Reserving is about liabilities for the past; pricing is about profitability for the future.
3.4 Deterministic Reserving Methods
3.4.1 Chain Ladder Method
The Chain Ladder method is a widely used, deterministic technique that captures claims development patterns through the calculation of link ratios or development factors (LDFs). These factors are derived from observed historical cumulative claims data (paid or incurred) and are used to project future claims development by assuming historical patterns will continue consistently across cohorts. It can be applied to paid or incurred data, generating distinct sets of LDFs. The method implicitly assumes that past development patterns are stable and predictive of future development. It is crucial to check for consistency and reasonableness of the LDFs.
3.4.2 Initial Expected Loss Ratio Method
This simple method assumes an expected ultimate loss ratio (ELR) for each cohort. It is particularly useful for very immature accident years where there is little claims experience on which to base development patterns from historical data, and thus relies heavily on underwriting expertise, pricing assumptions, and industry benchmarks. The ultimate loss is then estimated as ELR \times Earned Premium. While simple, it requires careful selection of the ELR and is less robust for mature years where actual experience is more credible.
3.4.3 Bornhuetter-Ferguson Method
The Bornhuetter-Ferguson (B-F) method blends the expected loss ratios (from the Initial Expected Loss Ratio method) with emerging claims experience (from methods like Chain Ladder) to stabilize projections. It places more weight on the expected loss ratio for immature years, recognizing that early claims data is often unreliable, and gradually shifts weight towards actual claims experience as the cohort matures and provides more credible information. This blending helps to reduce volatility and reliance on potentially unstable early development factors, providing a more balanced estimate than either method in isolation.
3.4.4 Average Cost Per Claim
This method breaks total losses into frequency (number of claims) and severity (average cost per claim) components for a more detailed analysis (TotalLosses = Frequency \times Severity). By analyzing these two components separately, actuaries can gain deeper insights into the drivers of claims costs, as different factors might influence frequency (e.g., changes in policy take-up, weather patterns) versus severity (e.g., medical inflation, legal precedents). This allows for a more granular understanding and projection of trends.
3.4.5 Comparing Results from Different Methods
Comparing results from different deterministic methods highlights the importance of not just averaging results, but understanding the differences due to various data treatments, underlying assumptions, and the inherent strengths and weaknesses of each method. This process, often referred to as 'triangulation', helps actuaries to identify potential biases, assumptions that may no longer hold (e.g., stable development in Chain Ladder), and arrive at a more robust and considered estimate. Significant divergences between methods can signal data issues or changes in claims patterns needing deeper investigation.
3.5 Reserving Uncertainty
3.5.1 Sources of Uncertainty
Sources of uncertainty include:
Model uncertainty: Arises from the choice of the reserving model and the assumptions inherent in that model (e.g., the assumption of stable development patterns in Chain Ladder, the choice of distribution for claims in a stochastic model). Different models can produce different results due to their inherent limitations or suitability for specific data characteristics.
Parameter uncertainty: Relates to the estimation errors in the parameters of the chosen model (e.g., the specific values of link ratios estimated from historical data, the expected loss ratios used in B-F, the variance parameters in a statistical model). These parameters themselves are estimates and are subject to sampling variation.
Process uncertainty: Represents the random fluctuations in future claim events themselves, such as the actual number of claims that will emerge (frequency risk) or the individual severity of those claims (severity risk), even if the underlying parameters and model structure were perfectly known. This is the inherent unpredictable variation in future claims experience.
External influences like economic conditions (e.g., inflation affecting repair costs and medical expenses, interest rates impacting discounted future payments), legal changes (e.g., new court precedents, reforms to tort law), social inflation (e.g., increasing jury awards, greater public awareness leading to more claims), and emerging risks (e.g., cyber liability, climate change impacts) also contribute significantly to reserving uncertainty, often requiring adjustments to historical data or specific scenario modeling.
3.5.2 Best Estimate
The best estimate is defined as the un-adjusted central estimate, representing the probability-weighted average of future cash flows. It is informed by all available data and a comprehensive understanding of future expectations, without any explicit prudential margins. It is a pure, unbiased estimate of the expected future payments, attempting to strike a balance between potential over-estimation and under-estimation.
3.5.3 Ranges
Different types of ranges are used to communicate uncertainty, providing stakeholders with a better understanding of the potential variability of loss outcomes:
Possible outcomes: A wider range that encompasses a broad spectrum of plausible future scenarios, including potentially extreme but still possible events. This range might be used for extreme stress testing or long-term strategic planning.
Reasonable outcomes: A narrower range representing the most likely or conventional outcomes, often corresponding to specific confidence levels (e.g., 75th or 80th percentile for internal reporting). This is typically what management focuses on for day-to-day operations.
These ranges help stakeholders understand the variability of the best estimate and are crucial for capital management (e.g., calculating reserves at a 99.5th percentile for solvency purposes under Solvency II, which requires capital to cover unexpected losses to a very high confidence level) and risk appetite setting.
3.5.4 Quantification of Range of Possible Outcomes
Approaches to quantify the range of possible outcomes include stochastic methods (as discussed in 3.6), conducting sensitivity analyses by varying key assumptions (e.g., increasing inflation rates by 1%, reducing development factors by 5% to see the impact on the reserve), and scenario testing (e.g., modeling the impact of a specific adverse event like a severe hurricane or a legal reform on reserves). These techniques help to simulate potential future states and measure their impact on reserve estimates, providing a distribution of possible outcomes.
3.6 Stochastic Reserving Methods
3.6.1 Uses and Benefits
Stochastic methods provide estimates of not just the point (best estimate) but also the variability around it, typically in the form of a probability distribution of ultimate claims outcomes. This offers a more comprehensive understanding of risk, enabling better decision-making in areas such as capital allocation (how much capital to hold against reserving risk), risk management (identifying key drivers of uncertainty), and pricing for risk (incorporating explicit risk charges). They are particularly valuable for meeting modern regulatory requirements like Solvency II, which demand quantification of reserving uncertainty and calculation of a capital charge for reserving risk.
3.6.2 Quantification of Prediction Error
Predicted values and their variances are calculated to determine the overall prediction error of the ultimate loss estimates. This error is typically decomposed into components such as:
Parameter risk: Uncertainty in the estimated model parameters (e.g., the true values of link ratios or the underlying mean claim severity). This arises because parameters are estimated from limited historical data.
Process risk: The inherent randomness of future claims development even if parameters were known perfectly (e.g., the actual number of claims that materialize or the severity of individual claims will always vary randomly around an expected value). This is the irreducible variability of future events.
Understanding these components helps actuaries to manage and communicate the different facets of uncertainty effectively.
3.6.3 Types of Stochastic Reserving Methods
Classifications are often based on:
Analytical vs. simulation methods: Analytical methods use statistical theory to derive probability distributions for ultimate claims directly (e.g., Mack's Chain Ladder model, which provides a formula for the mean squared error of prediction). Simulation methods (e.g., Bootstrapping Chain Ladder, Bayesian MCMC methods) involve repeatedly drawing samples from the data or fitted distributions to build an empirical distribution of outcomes, especially useful for complex or non-standard models.
One-year vs. ultimate time horizon: One-year risk is concerned with the change in reserves over the next year, often used for regulatory capital calculations (e.g., Solvency II capital requirements are typically based on a one-year view of risk). Ultimate risk focuses on the total final cost of claims, encompassing all future development until closure, which is critical for financial reporting and long-term solvency assessment.
3.7 Reserving Results
3.7.1 Diagnostics
The importance of various diagnostics lies in validating reserving results over time against the underlying assumptions. This includes assessing the consistency of estimates, the stability of development patterns, the reasonableness of projected ultimate losses, and the impact of changes in external factors or internal operations. Diagnostics help to build confidence in the estimates and flag areas where assumptions might be breaking down, requiring adjustments.
3.7.2 Development Pattern Diagnostics
These diagnostics are crucial in determining if historical development patterns still apply in the current environment. They involve examining the stability of link ratios over time (e.g., year-on-year changes in Chain Ladder factors), identifying any changes in settlement speed or claims reporting (e.g., faster claims closure initiatives, new reporting requirements), and recognizing impacts of operational changes (e.g., new claims handling software) or shifts in business mix. Visual tools like heatmaps of link ratios or plots of implied ultimates from different ages are often used.
3.7.3 Analysis of Emerging Experience
This involves continuously comparing new estimates with past assumptions and observed data trends. Key analyses include "actual vs. expected" comparisons (e.g., comparing actual paid claims against what was expected to be paid in the last valuation), reviewing deviations from prior valuations (e.g., whether the IBNR for a specific year is developing adversely or favorably), and analyzing changes in claims closure rates or severity trends. This feedback loop is essential for refining models and assumptions for future valuations.
3.7.4 Underwriting Cycle
The underwriting cycle refers to the cyclical fluctuations in pricing, availability of coverage, and profitability in the insurance market (periods of 'soft' market with low premiums and loose underwriting vs. 'hard' market with high premiums and tighter underwriting). It requires actuaries to adjust historical loss ratios and development patterns to reflect prevailing underwriting conditions and avoid erroneous projections based on non-representative historical periods. For example, claims from a soft market year might develop differently (e.g., higher ultimate loss ratios) than those from a hard market year.
3.7.5 Comparing with Other Estimates
Assessing divergences against benchmarks, such as internal estimates from different departments (e.g., claims department's view, pricing actuary's view), external market data, or peer review results, is essential to ensure the robustness and credibility of the reserving estimates. This helps to identify outliers, confirm assumptions, and build confidence in the final figures by triangulating from multiple perspectives.